How to make sense of the Leximancer analysis: a quick
guide to interpreting concept maps
This quick guide has been developed to support the research
processes of those who are new to the semantic analysis software
Leximancer and would like to make a prompt start with interpreting the
data. The guidance is mainly focused on making sense of the concept
maps and gives some tips on what to look for when exploring the data.
Although a couple of ‘how to do’ bits of advice are also included,
generally we assume that the researcher would know how to reach
the ‘output’ (actual concept map) stage. This guide is based on the
latest Leximancer 4 version, so the interface could be slightly different
from the early versions of the software, but the general principles of
the analysis and core functions of the software remain the same.
1. What is Leximancer?
This is a semantic analysis software that has been developed at the
University of Queensland, Brisbane in 2001
(https://www.leximancer.com/).
2. Why/when would you want to use the software?
Leximancer is a useful instrument for researchers/analysts who need
to explore a large text-based data set where manual analysis and
coding would be too time consuming, e.g. qualitative survey data,
multiple interview/focus group transcripts, lengthy reports or webbased textual information.
3. How does it work?
The process is called unsupervised semantic mapping of natural
language. The method can also be thought of as a form of text mining.
Leximancer employs two stages of information extraction: semantic
and relational, using a different algorithm for each stage. It computes
the frequency with which each word is used and then calculates the
distance between each of the terms (co-occurrence). The algorithms
used are statistical, but they employ non linear dynamics and machine
learning. The results of computations are displayed as a concept map
that can be explored on individual concept levels and also by looking
at the family of associations between different concepts (themes).
4. What are the benefits of using the software?
Leximancer provides a fairly unbiased method of reviewing complex
textual data sets and a clear process of justifying decisions about text
selection. It makes the researcher aware of the ‘global’ context and
helps to discover ‘hidden’ structures in text that fall outside of his/her
preconceived framework.
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The automated analysis demonstrates a stability of measurement over
time and what is most important - it allows a more rapid and frequent
exploration of text with reduced cost. Tailored (researcher driven)
analysis also could be done if the researcher would like to explore
specific topics/concepts.
One of the attractive features of the software is its ability to identify
sentiments by showing probability of a concept being mentioned in a
favourable or unfavourable context. Leximancer could be used on its
own or as a complementary tool for other methods of analysis.
5. Anything to be aware of/any limitations?
There are some limitations to ‘purely automated’ analysis. Some
concepts emerge strongly where they are represented by a narrow
vocabulary. Others will be identified from a broader pool of terms and
have a greater likelihood of being diluted as concepts in the map. This
can be mitigated by the manipulations with a thesaurus (e.g. adding
concepts that haven’t make a relevance threshold to the concept map,
creating combined/compound concepts etc) and should be
acknowledged as a researcher-driven analysis.
Leximancer findings might benefit from being combined with outcomes
from other type of analysis such as traditional thematic analysis or
content analysis for gaining a more comprehensive picture.
Some more insights into the software and how it works could be found
on
http://www.textinsight.net/sites/default/files/files/What%20is%20Lexim
ancer.ppt
The most exciting part is actually using the tool!
First of all...
First of all you need to run a preliminary analysis of your ‘unspoiled’
data set, with no editing or configuration, to get a ‘feel’ of the data.
If you are using the software for the first time, for a step by step
guidance of how to make a start look at the PowerPoint presentation
created by Julia Cretchley and Mike Neal
http://www.textinsight.net/sites/default/files/files/First%20Leximancer%
20Analysis.ppt
6. How to make sense of your first concept map
Concept level exploration
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1. Take some time to visually explore the concept map generated (you might want to
set the Theme Size scrolling Bar on 0% (see Fig. 1), so that you can focus on the
concepts first.
Fig 1.
2.
When you have located the most relevant concept (largest circle), you will instantly
know what is of the most importance to either your respondents/research participants
or to the text creators. Pay attention to the concepts positioned closely to the most
relevant concept - those with direct links/connections would indicate that these two
words are often used together and worthy of detailed exploration.
Things to note: Watch out for language your research participants use.
Students often use different terms to describe the same concept - it
might give you an indication of a specific connotation or a context in
which they choose to use a particular word. For example while
describing academic staff, students use different words such as staff,
lecturers, tutors, teachers etc. What is interesting is that these
concepts/themes are not always located close to each other and
associated with a different contextual and often emotional
background.
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3. Explore other concepts (those that constitute ‘nodes’ in the concept map). Their
relevance is reflected by size (more relevant ones have bigger size). Note where
they are positioned on the map and which concepts they are surrounded by. This
could give you an idea of other significant themes and how they are connected.
4. Look for concepts that are less important/could be excluded from your analysis,
(these, for example, could be frequently mentioned words such as ‘certain’, ‘have’
etc). Some concepts could be merged (e.g. placement and placements) or made
compound (e.g. work + experience). You could later make these changes to make
your concept map more readable
5. Explore the Concept Ranking Table located to the right from the concept map (Fig 2).
The relevance of the concepts will be indicated in the table numerically (number of
instances/actual quotes and relevance %).
Fig 2.
6. By clicking on a concept on the map you can see how this concept is related to
others. It will be shown via graphical links and also as a table indicating the likelihood
of other concepts being mentioned together with the concept in question (Fig 3).
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Fig 3.
7. When you familiarised yourself with the layout of the map and have a broad
understanding of the concepts that are coming out from the data and how they are
connected, it's time to explore the direct quotes/instances that formed these concepts.
Go to the Concept Ranking Table (located to the right from concept map) and under
the Count heading click on a number that corresponds to the concept you would like
to explore (Fig 4). You will have instant access to all the quotes that contributed to
the creation of the concept. The quotes will give you an indication of the meaning(s)
behind the concept and essentially how ‘semantically clean’ is the concept. For
example word ‘feedback’ would most likely have a singular meaning (Fig 5), while
‘work’ might have multiple meanings, since students, for example, could talk about
their course work, part-time work, work related learning, work as verb – e.g. ‘work it
out’ etc. In case of multiple meanings you might want to explore only specific
links/connections with other concepts that are of interest for your research or make
some adjustments on the next, advanced stage of the data processing.
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Fig 4.
Things to note: Not all possible quotes where the concept (word) was
mentioned in the text will be included in the list. Only those that
passed the relevance threshold will be listed.
Fig 5.
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8. If you are interested in quotes/instances where two specific concepts were
mentioned together, click on a particular concept (under WordLike) in the Concept
Ranking table, and when a list of related concepts appeared with the icon
, click
on the icon attached to the concept that you would like to explore together with the
first concept. (Fig 6)
Fig 6.
Exploration of themes
Themes are concept clusters that represent the most semantically
connected groups of concepts. Theme name is the most prominent
concept in the cluster.
1. Set your Theme size scroll bar to 100% - to see the concept(s) with the highest level
of connectivity – these will be the most important themes that are coming out from
your text (Fig 6). There might be one theme, two or, sometimes, more – they all are
worth paying close attention. You could explore relevance of the themes and, as in
the case of concepts, have access to all quotes that are illustrative of the theme.
Things to note: If you‘ve got more than one theme at 100% resolution,
look for the concepts that are positioned in the intersected area(s) –
they might give you some insights of what topics ‘belong’ to both
themes.
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Fig. 7.
2. When reducing percentage on the scroll bar, more themes (concept clusters) will
appear on the map (Fig.8). Look carefully at the dynamics of transformation - e.g.
how smaller themes merge into bigger ones, what themes disappear and what
remain.
3. Remember or better save the themes generated by the automatic analysis. It is
possible that when you made some custom configuration changes, the thematic
picture will also change.
Fig.8
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Next step…
The next step is making necessary adjustments for a more
focused/customized analysis, for example, removing concepts that are
not central for your research, merging or adding the concepts,
activating sentiment analysis, adding tagging etc.
When all the changes you think might be useful for your research are
done, the analysis should be run once more, following the same
stages that were indicated/described above.
7. Sentiment analysis
Activating the sentiment lens can be a bit tricky, so below is a quick
step by step guide how to do it.
1. To enable sentiment analysis, in the Project Control Panel select ‘Generate
Thesaurus’ and then activate ‘Show Settings’.
2. Click on Concept Seeds’ and then on ‘Edit’ (Fig 9).
3. Choose User Defined Concepts from the top menu and then click on Sentiment
Lens button that is activated/highlighted. Favourable (favterms) and Unfavourable
(unfavterms) terms will appear under the Concept list (Fig 10).
4. Save the settings by pressing ‘ok’ and then run your analysis from scratch so that
sentiment analysis is incorporated.
Fig. 9
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Fig.10
Why use the sentiment analysis?
Sentiment analysis gives you an instant indication of probability of a
concept being mentioned in a favourable and unfavourable context.
Just click on a concept in the Concept Ranking Table and see the
results. The difference could be relatively small (as in Fig 11 – just 1%
or under) or quite noticeable. The latter case will be an evidence of a
particular strong disposition of your research participants.
By clicking on
favourable/unfavourable you will have access to
all quotes that were selected as indicative of the particular sentiment
for this concept.
Things to note: Don’t be surprised if, for example, the percentage of
unfavourable likelihood is higher, but number of quotes selected by
the software will be the same than in case of favourable quotes. This
is not a direct/proportional relationship, as it is determined by various
statistical procedures and indicative of more complex relationships
between the concept and strength of the sentiments expressed.
Things to note: Pay attention to the location of favourably and
unfavourably rated concepts on the map. Sometimes the same
sentiment concepts cluster together, this would be an indication of a
strong shared concerns/issues. It is also useful to explore themes in
relation to dominant sentiment background of the concept that form
the theme.
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Fig 11.
Final remarks
You could use some advanced functions useful for the analysis - for
example tagging files and groups of files to allow for comparison,
creating Dashboard Reports, explore word cloud, look at your concept
map from the ‘social network’ perspective etc. Instructions how to do it
can be found in the ‘Help’ section of the software. Some advance
features are also explained in
https://www.leximancer.com/dl/training/2011-12-V4194059105-AB-45J/LeximancerIn-depth.ppt
The main and most time consuming work will be about delving into the
actual quotes and extracting meanings of the concepts and concept
connections, making sense of themes and general landscape of your
text.
When you have had a chance to compare the outcomes of a ‘fully
automated’ analysis and a researcher-driven, customized one you
might decide that automated analysis is more insightful. It is easier to
start everything from scratch (as a new project) rather than trying to
‘undo’ the changes that were made.
Comparative analysis of concept maps
Leximancer could be a helpful tool when you need to compare two
data sets or to look at the data longitudinally. Look for new, emerging
concepts and concepts that have disappeared, explore how the
relevance of the concepts and themes have changed, what happened
to direction and strength of the sentiments for the key concept, how
positioning of the main concepts and their neighbours changed.
Comparative analysis could be especially insightful!
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Please send your questions or comments related to this guide to Dr
Elena Zaitseva (e.zaitseva@ljmu.ac.uk)
7. References
1. Smith, A. & Humpreys. M., 2006, ‘Evaluation of unsupervised semantic mapping of
natural language with Leximancer concept mapping’, Behavioural Research
Methods, (38), pp. 262–79.
2. Dodgson et al., 2008 ‘Content analysis of submissions by Leximancer’. Available at
http://www.innovation.gov.au/Innovation/Policy/Documents/LeximancerSubmissionA
nalysis.pdf (accessed 15 April 2013)
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